CS 115: COMPUTING FOR The Socio-Techno Web image source: http://www.igenii.com/blog/Social%20Media/social- media/ twitter.com http://www.worthofweb.com/blog/case-study-this-revolution-will-be- tweeted/ http://foxwoodonlinemarketing.typepad.com/my-blog/social-media/ CS 115: COMPUTING FOR The Socio-Techno Web The fun and the fear of Online Social Networks

Today Socio-techno web wrap-up: Social networks: six degrees of separation, tie strength, homophily Social algorithms: filter bubble, personalization, bias Where to go from here: Redo questions from into questionnaire Digital humanities at Wellesley Other CS classes at Wellesley

SOCIAL Graphs NICOLE

Diameter The diameter is 3. 5 7 8 6 3 2 4 The diameter is 3. 1 Definition: The diameter of a graph is the maximum shortest-path distance between any two nodes.

The diameter of a social network is small. Milgram’s experiment (1960s). Ask someone to pass a letter to another person via friends knowing only the name, address, and occupation of the target. 296 People in Omaha, NE, were given a letter, asked to try to reach a stockbroker in Sharon, MA, via personal acquaintances

Small world phenomenon Bernard, David’s cousin who went to college with David, mayor of Bob’s town Bob, a farmer in Nebraska Maya, who grew up in Boston With Lashawn

Milgram: Six Degrees of Separation 296 People in Omaha, NE, were given a letter, asked to try to reach a stockbroker in Sharon, MA, via personal acquaintances 20% reached target average number of “hops” in the completed chains = 6.5 Why are chains so short? I know! Exponential growth of friends! That’s the standard explanation is it true? Erdos studied random graphs… But is acquaintance-relationship just *any* random graph?

Today we can measure this with 2 billion people https://research.fb.com/three-and-a-half-degrees-of-separation/ Our online traces, or “digital footprints”, let us measure things today at a large scale and confirm or disprove sociological theories. The majority of Facebook users (~2 billion people) have an average between 3 and 4 steps.

Why are Chains so Short? If I count my friends’ friends’ friends… 1 2 22 But things are not as simple, since most of my friends are friends with each other! + 2d 2d+1 - 1  diameter = log n

Kevin Bacon number = distance between actor and Bacon The Kevin Bacon Game nodes = {actors} edges = if two actors star in same film Kevin Bacon number NICOLE Kevin Bacon number = distance between actor and Bacon

The Kevin Bacon Game Invented by Albright College students in 1994: Craig Fass, Brian Turtle, Mike Ginelly Goal: Connect any actor to Kevin Bacon, by linking actors who have acted in the same movie. Oracle of Bacon website uses Internet Movie Database (IMDB.com) to find shortest link between any two actors: http://oracleofbacon.org/ Search for Paul Erdos! Degree 3! In credits see that the original creator of the site was Brian’s brother.

Title Data

Math PhD genealogies Famous distances 14 generations to Euler! Mostly a tree. A few weird relationships (a triangle to the mid-right, a few cycles in previous centuries. Joint advisors.) You can divide it into countries!

Erdős number = distance between mathematican and Erdos Famous distances nodes = {mathematicians} edges = if 2 mathematicians co-author a paper Paul Erdős number NICOLE Erdős number = distance between mathematican and Erdos

Erdős Numbers Erdős wrote 1500+ papers with 507 co-authors. Number of links required to connect scholars to Erdős, via co- authorship of papers What type of graph do you expect? Jerry Grossman (Oakland Univ.) website allows mathematicians to compute their Erdős numbers: http://www.oakland.edu/enp/ Connecting path lengths, among mathematicians only: avg = 4.65 max = 13

Ron Graham’s hand-drawn picture of a part of the mathematics collaboration graph, centered on Paul Erdo ̈s [190]. (Image from http://www.oakland.edu/enp/cgraph.jpg)

Proud people!

Famous distances Erdős number of … = 4

What type of graph to expect? Famous distances Erdős number of … = 3 What type of graph to expect? A tree? Clustered? Chung, F. R. K.; Erdős, P.; Graham, R. L. On the product of the point and line covering numbers of a graph. Second International Conference on Combinatorial Mathematics (New York, 1978), pp. 597-- 602, Ann. New York Acad. Sci., 319, New York Acad. Sci., New York, 1979. Chung, Fan R. K.; Leighton, Frank Thomson; Rosenberg, Arnold L. Embedding graphs in books: a layout problem with applications to VLSI design. Graph theory with applications to algorithms and computer science (Kalamazoo, Mich., 1984), 175--188, Wiley-Intersci. Publ., Wiley, New York, 1985. Andrews, Matthew; Leighton, Tom; Metaxas, P. Takis; Zhang, Lisa Automatic methods for hiding latency in high bandwidth networks (extended abstract). Proceedings of the Twenty-eighth Annual ACM Symposium on the Theory of Computing (Philadelphia, PA, 1996), 257- -265, ACM, New York, 1996. Fan Chung F.T. Leighton P.T. Metaxas Erdos

YOU = Bill Gates Famous distances Erdos number of … if you publish with me! YOU = Bill Gates

Tie strength Not all friendships are created equal... How strong is your relationship with this person? How would you feel asking this friend for $100? How helpful would this person be if you were looking for a job? Self-reported high school friendships [Moody 2001] A racially diverse student body in American high schools may not lead to more friendships between students of different races, according to a new national study. Results showed teens tend to choose same-race friends, even as the opportunity to choose friends from different races increases. However, school practices regarding academic tracking, extracurricular activities and student mixing by grade can help promote friendships among students of different races, the research found. Probability of your friends being friends is based on number of links in your neighborhood over all possible links (the size of your neighborhood - choose - 2 The clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are to being a clique (complete graph). The strength of weak ties [Granovetter 1973]

Tie strength Self-reported high school friendships [Moody 2001] A racially diverse student body in American high schools may not lead to more friendships between students of different races, according to a new national study. Results showed teens tend to choose same-race friends, even as the opportunity to choose friends from different races increases. However, school practices regarding academic tracking, extracurricular activities and student mixing by grade can help promote friendships among students of different races, the research found. Probability of your friends being friends is based on number of links in your neighborhood over all possible links (the size of your neighborhood - choose - 2 The clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are to being a clique (complete graph).

Self-reported high school friendships [Moody 2001] A racially diverse student body in American high schools may not lead to more friendships between students of different races, according to a new national study. Results showed teens tend to choose same-race friends, even as the opportunity to choose friends from different races increases. However, school practices regarding academic tracking, extracurricular activities and student mixing by grade can help promote friendships among students of different races, the research found. Probability of your friends being friends is based on number of links in your neighborhood over all possible links (the size of your neighborhood - choose - 2 The clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are to being a clique (complete graph).

Self-reported high school friendships [Moody 2001] A racially diverse student body in American high schools may not lead to more friendships between students of different races, according to a new national study. Results showed teens tend to choose same-race friends, even as the opportunity to choose friends from different races increases. However, school practices regarding academic tracking, extracurricular activities and student mixing by grade can help promote friendships among students of different races, the research found. Probability of your friends being friends is based on number of links in your neighborhood over all possible links (the size of your neighborhood - choose - 2 The clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are to being a clique (complete graph).

Homophily Birds of a feather flock together... Homophily Self-reported high school friendships [Moody 2001] A racially diverse student body in American high schools may not lead to more friendships between students of different races, according to a new national study. Results showed teens tend to choose same-race friends, even as the opportunity to choose friends from different races increases. However, school practices regarding academic tracking, extracurricular activities and student mixing by grade can help promote friendships among students of different races, the research found. Probability of your friends being friends is based on number of links in your neighborhood over all possible links (the size of your neighborhood - choose - 2 The clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are to being a clique (complete graph). Self-reported high school friendships (edges) and race (colors). [Moody 2001]

Birds of a feather flock together... Homophily Social influence Network dynamics Confounds Self-reported high school friendships [Moody 2001] A racially diverse student body in American high schools may not lead to more friendships between students of different races, according to a new national study. Results showed teens tend to choose same-race friends, even as the opportunity to choose friends from different races increases. However, school practices regarding academic tracking, extracurricular activities and student mixing by grade can help promote friendships among students of different races, the research found. Probability of your friends being friends is based on number of links in your neighborhood over all possible links (the size of your neighborhood - choose - 2 The clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are to being a clique (complete graph). Self-reported high school friendships (edges) and race (colors). [Moody 2001]

Birds of a feather flock together... Homophily Adamic, L. A., & Glance, N. (2005, August). The political blogosphere and the 2004 US election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery (pp. 36-43). ACM.

Social Algorithms

Filter Bubble -

Personalization

Bias

Future of the Internet Pew Research Center, 2014

Future of Privacy

Future of Politics

Future of Computing

Future of AI Ethics Responsibility Ownership

Today Socio-techno web wrap-up: Social networks: six degrees of separation, tie strength, homophily Social algorithms: filter bubble, personalization, bias Where to go from here: Redo questions from into questionnaire Digital humanities at Wellesley Other CS classes at Wellesley

Computer quizz Redo questions from into questionnaire

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

Who you are Computing is a highly collaborative field. And our goal this semester is to create a community for learning. You will collaborate in different ways – lets start by introducing yourself. Here is overview on our collaboration policy, and it is followed by a more detailed explanation below:  Assignments: Rotating pairs of students  Project: Teams of 2-3 students  Exams: Absolutely no collaboration

WHat next http://www.wellesley.edu/cs/curriculum/current: CS111 Computer Programming and Problem Solving CS220 Human-Computer Interaction Later: CS230 Data Structures CS232 Artificial Intelligence CS342 Computer Security and Privacy CS304 Databases with Web Interfaces

WHat next https://www.wellesley.edu/lts/bli/projects Wellesley Blended Learning Initiative “The initiative encompasses the innovative use of technology in teaching, conversations about the use of technology to support close faculty-student relationships in a liberal arts environment, and the establishment of a long-term organizational support structure for blended learning at Wellesley College.”

Additional resources Practicing HTML, CSS, Javascript: http://www.stackoverflow.com https://www.w3schools.com/ Getting started with other programming languages: https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11 https://codecombat.com/